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Automatic Target Recognition in high resolution Optical Aerial Images

Automatic Target Recognition in high resolution Optical Aerial Images. Xavier PERROTTON Marc STURZEL Michel ROUX Xavier.perrotton@eads.com marc.sturzel@eads.com michel.roux@enst.fr Image & Signal Processing Laboratory Telecom Paris.

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Automatic Target Recognition in high resolution Optical Aerial Images

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  1. Automatic Target Recognition in high resolution Optical Aerial Images Xavier PERROTTON Marc STURZEL Michel ROUX Xavier.perrotton@eads.com marc.sturzel@eads.com michel.roux@enst.fr Image & Signal Processing Laboratory Telecom Paris 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  2. Why ATR for EADS? • Objective : make a breakthrough on ATR in visible images • Context • Observation systems (satellites, UAVs, aircraft…) • Huge volume of data sent back by current and future systems • Limited number of operators • Pressure to shorten the loops • Autonomous systems (missiles, UAVs…) • More intelligence onboard • Strong need in the future for : • Fully automatic processing • Autonomous systems • ATR still unsolved for operational use 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  3. The problem • Challenging problems • Lighting, occlusion and background • Difficult segmentation • Targets size • Local descriptors approach • Learning appearance characteristics • Focusing on discriminative parts of the target 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  4. Questions • Can we efficiently use local descriptors? • How to extend application domain by statistical learning? 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  5. 1 Selecting and learning keypoints Recognized target 2 Generating a list of candidate matches Descriptor : GLOH (Gradient Location orientation Histogram) 3 Defining an hypothesis 4 Hypothesis propagation Local descriptors: method 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  6. Descriptors GLOH (Gradient Location orientation Histogram) [1] How to match Keypoints? Looking for the best match on each pixel Associating a limited number of matched points for each learned keypoint How to define an hypothesis ? Choosing three points among the best matches Evaluating the affine transform How to propagate an hypothesis? Checking for agreement between each candidate point and the geometric model Local descriptors: method [1] K. Mikolajczykand C. Schmid. A performance evaluation of local descriptors. In Proc. IEEE CVPR, June 2003 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  7. Local descriptors: tests on real images (1) Learned target Matched targets 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  8. Local descriptors: tests on real images (2) Learned target Matched targets 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  9. Local descriptors: tests on real images (3) X • Aerial Images difficulties : • Few points • Not robust to background • We must find a way to learn the variability of appearance characteristics 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  10. AdaBoost: a powerful learning concept • Principle : • Iterative learning algorithm introduced by Freund and Schapire [2] • Constructing a “strong” classifier in combining “weak” classifiers • Selecting a “weak” classifier at each iteration • Used for face detection by Viola and Jones [3] • Advantages : • often outperforms most “monolithic” strong classifiers such as Neural Networks • Few parameters to tune [2] Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. 97 [3] Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features.IEEE CVPR, 2001 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  11. AdaBoost: algorithm • Adaboost starts with a uniform distribution of “weights” over training samples • We obtain a weak classifier from the weak learning algorithm, hj(x) at each round • We compute j that measures the confidence assigned to hj(x) • We increase the weights on the training samples that were misclassified • Repeat • At the end, make a weighted linear combination of the weak classifiers obtained at all iterations 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  12. Weak classifier Database Positive, negative samples Feature output database X X X X X X Feature + Threshold = weak classifier X X X Feature X X X X • A weak classifier is only required to be better than chance • Very simple and computationally inexpensive X X X X • Haar like features • Gabor filters • Steerable filters • orientation estimation features… X 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  13. Database • Generation of a representative database with positive and negative samples • The classifier is learned on images of fixed size • Detection is done through a sliding search window • Angle variations : -5° to 5° 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  14. Tests on real images Learned different appearance characteristics successfully 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  15. Descriptors • Challenge : Finding descriptors less sensitive to background and target texture • Haar like features learn only difference of contrasts • Not enough to discriminate complex textures • But can be very efficient on shadow • Gabor filters, steerable filters, orientation estimation features • More robust to background and target texture 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

  16. Conclusion • Local descriptors enable to define an efficient ATR algorithm • Targets can be modelled as a collection of regions • Geometric constraints are efficient to eliminate false alarms • Statistical learning enables to extend the application domain • Selecting the discriminating features • Learning the variability of appearance characteristics • Descriptors • To detect particular oriented edges • To detect different regions 7th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen

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